A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices

Abstract

Accurate forecasting for the crude oil price is important for government agencies, investors, and researchers. To cope with this issue, in this paper, a new paradigm is designed for the reconstruction of intrinsic mode functions (IMFs) of decomposition and ensemble models to reduce the complexity in computation and to enhance the forecasting accuracy. Decomposition and ensemble methodologies significantly enhance the forecasting accuracy under the framework of “divide and conquer” with the proposed reconstruction of IMFs method. The proposed approach used the autocorrelation at lag 1 of all IMFs for the reconstruction. The ensemble empirical mode decomposition (EEMD) technique is employed to decompose the data into different IMFs. Models that utilized the decomposed data relatively perform well, as compared to its application to the undecomposed data. However, sometimes, the decomposition may produce poor results due to the error accumulation at the end. Thus, in this study, the reconstruction of IMFs is proposed for minimizing the aforementioned error, thereby increasing the forecasting accuracy. The Brent and West Texas Intermediate (WTI) datasets (daily and weekly) are exploited to compare the forecasting performance of autoregressive integrated moving average (ARIMA) along with artificial neural network (ANN) models with the decomposed data. The results have proven that the new paradigm of reconstruction of IMFs through autocorrelation was a better and simple strategy that significantly improved the performance of single models including ARIMA and ANN. Hence, it is concluded that the proposed model takes less computational time and achieved higher forecasting accuracy with the reconstruction of IMFs as opposed to using all IMFs.

A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

“…us, for forecasting the crude oil prices, the MEEMD is recommended for data decomposition. e model MEEMD-GMDH outperforms all of the models for a different number of observations, that is, 500, 1000, 2000, 5000, and 10000. e MAPE values of the model MEEMD-GMDH are less than 1 for all sets of observations which demonstrated the highly accurate forecasts [37,38]. e experimental findings of both scenarios demonstrated that all ensemble methodologies were effective but MEEMD was more effective.…”

“…e MEEMD and EEMD procedure divided the original time series into IMFs in such a way that the first IMF is more stochastic as compared to the second IMF and the second IMF is more stochastic than the third IMF and so on, whereas the last IMF is completely deterministic. Synthetic time series datasets which are composed of additive white noise and sine function are described in two different scenarios as follows [37][38][39]:…”

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